{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\programdata\\anaconda3\\lib\\site-packages (1.0.1)\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2019.3)\n",
      "Requirement already satisfied: numpy>=1.13.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (1.18.1)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2.8.1)\n",
      "Requirement already satisfied: six>=1.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from python-dateutil>=2.6.1->pandas) (1.14.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#1. iris数据集，画出sepal_length字段，将直方图和密度图画在一张图里。\n",
    "import matplotlib.pyplot as plt\n",
    "url = 'https://www.gairuo.com/file/data/dataset/iris.data'\n",
    "df = pd.read_csv(url)\n",
    "\n",
    "ax = df['sepal_length'].plot.hist(density=True)\n",
    "df['sepal_length'].plot.kde(ax = ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f3eeecd7c8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3j7Fjx3Lw4EHCw8NJSUnhp59+IjY2lubNm9O7d29ZWlmSyghVSAiho8egCg7Gef48bN9885FP8r8E/cJnpz/DuYozP7z4Q4kugFIU5BO5xcjf358ff/yRjRs3MmrUKEaMGJFbRvnSpUv07NkTkKWVJaksSD9zhrDxE0AIPL79Fou2bfJsz8zOZNHJRey8tZPOrp35rPNn2JjalFJv9VchZs+UVWvWrGHAgAGkp6djaWlJkyZNOHnyJBkZGZibm+cut/i00sr3SzRLklR87v36K8HvjMDQ3h7Pn396JPDDU8N5a89b7Ly1k9HNRrOy28pyGfggr/T1kp2djaIoj10jF3SrZd25c4c7d+7QqlUroqKiCAwM5PXXXwfgm2++wcTEhJdffhkbGxtZWlmSSonQaIhZ+iUJmzZh0bEjrr7LHpmOeTziONOPTEer1bKy60qec3+ulHpbNGTo62Hz5s2sX78+d1jGzMws90Yu6K7ce/fuzcyZMwFwdnZm3759AAQFBTFw4EB27NiBjY3uSuHhJRcfLq08ZcqUkjolSap0NKmpRHwwldTDh7EbMgSnmTNy69+D7gLs28vfsuLcCmrZ1OKr57/Cw7r8VwmQpZVLmBAClUqFqalpoY5T3r8PklSaVGFhhI0eTdbtOzjPmY3dgAF5tqeqUpl9bDb7Q/bTy7MX8zvMp4pxlVLqbcHJ0spliKIohQ58SZL0lx4QQNi48QiNBo8N32DRvn2e7SHJIYw7MI6Q5BCmt57OkAZDKtSEChn6kiRVGvd+30HU3LkYu7jgtnYNpjVr5tl+JuoMkw9NRkHhm//7pkyVRC4qMvQlSarwhEZDrK8v8Ru+pUr7drgtX46hTd7ZN78G/conJz/Bw9qDlV1X4m7tXkq9LV4y9CVJqtA0qWlETJ9O6oED2A4cgPNHeevfa7Qalvov5YfAH+jo0pElzy3BysSqFHtcvOQ8fT2tXbuW27dvo9Vq0Wg0ue0ajYaUlBS0Wi1CCE6fPs3IkSNzt7do0QLQ3dB9eH5+RkYGWq2WDz74gH///ReAsLCwEjobSaqY1OHhBA8eTOqhQzjNnk31efPyBH6qKpVxB8bxQ+APDGkwhJXdVlbowAd5pa+3Zs2a0b9/f95//33WrVuHSqUiMjKSWrVqYWBgwKhRo9i0aRMpKSlERUXh4+MDwM2bN/Hx8UGr1TJ27FjefPNNsrKy6N+/PxMnTsTQ0BBjY2OSkpJ4//336devH2+//XbpnqwklUPp587pbtiqVLoKmZ065tkemhLK+P3jCU4OZk67OfSv17+UelqyynXoLz69mGsJ14r0mPXt6zOjzYyn7pOamkrbtm05ffo0BgYGvPfee5w/f57ly5fnqaQ5bNgwzp07x44dO7CysmLixIkMGjSIn3/+OXc2QFZWFn369GHChAl0796dvXv3cvz4cT799FOmTp3K33//TXh4OK6urk/ojSRJ/5W0axeRs2ZjVL067t9/h2mtWnm2+0f5M/nQZLRCy9oea2lbvW0p9bTkyeEdPfzzzz94e3uzf//+p+7Xrl07Jk2axOHDh1m0aBE9evQgNjY2zy8GU1NTduzYgZubG/PmzePXX3/l8uXLTJkyBRsbG+bNmycDX5LySWi1xCxfTsT0GZh7e+P504+PBP7vN37nvb3vYWtqy7be2ypV4EM5v9J/1hV5cXn99dfx8fEhOzv7qfsdOHCAa9eu4eXlxSuvvML+/fv5+eefuXTpEtevX6devXocOXKEBQsWEBQUxKxZs/jwww8BCA8PZ/369YwdO5bu3buXxGlJUrmmzcwk4sMPSdnzNzb9Xqf63LkoDy1upNFqWH52OZuvbKZd9XYsfW5pua2fUxjlOvRLk42NDW3atOHAgQPY2to+dp81a9aQlJRE06ZNaZ/zAEh8fDyKojBo0CD27NlDly5d2L9/P71792bbtm15Km4GBwfLgmuSlA/ZsbGEjh1H5qVLOE6biv077+R5oCpNncaMIzM4HHaYAfUGMKPNDIwMKufPVuU86yKwefNmunbt+sTAv3XrFv7+/mzYsIGuXbvmLpoSHx9PixYt6N69O9HR0Tg6Oua+Ztq0aRg/NLNg+/btxXsSklQBZF6/Tujo0WgS7+G24mus/vPJODw1nHH7x3En6Q4ftf2IgfUHllJPy4Z8h76iKKuBPUKIPxRF+RZoCPwphFiUs13vtvImLi6O+fPnc+LECZo3b46BgQEajYbY2FjatWuXu67t3Llz6dmzJ1OmTKFGjRoEBARw/PhxunXrlnvlf9/D0z4f9riyy5Ik6aQePkz45CkYWFpS44fvMW/UKM/2czHnmHRwEmqNmtXdV9PBpUMp9bTsyFfoK4rSGXDOCfzXAEMhRHtFUTYqiuIFNNG3TQhxo7hOrrgYGBjw+eef4+7uzvnz55+434YNG/j44495/vnnATh79ix9+vShbdtHbxxlZ2ezZMmSR4Z3Bg6s3FclkvQ4QggSv/+B6M8/x7R+PdzXrMHYySnPPrtu7WL+8flUt6jOip66SplSPqpsKopiDFwC/gIOA92Av4UQfymKMgAwB7z1bRNCbHrSe1fEKptPolKpMHnoptOzVNTvgyQ9i8jOJvrTT0ncth3L7t1w/eILDKo8qICpFVpWnFvBhksbaOPchmU+yyrdDdunVdnMz5TNt4CrwBdAG2AsEJ6zLQFwAiwK0fbfzo5UFMVfURT/2NjYfHSvYihI4EtSZaVJSSH0/VEkbtuO/Yh3cPv66zyBr9ao+ejoR2y4tIHXvV5nbY+1lS7wnyU/wzvewHohRJSiKD8AHdBdtQNYovvFkVqItjyEEOuB9aC70i/g+UiSVEGpwsIIHTUK1d1gqi9aiG2/fnm2p6pSmXxoMicjTzLBewLvNnm3QpVELir5udK/CdwfDGsFeAKdcv7eDLgLBBSiTZIk6anSz57jbv83yY6Nw2PDhkcCPzY9lrf/eZszUWdY2HEh7zV9Twb+E+TnSv9bYGPOGLwx4APsUhTFBegFtAME4KdnW7l34cIFUlJS6NSp0xP3+eWXX+jbty9mZmYl2DNJKv+S/thN5KxZGFV3xn3t2kdq4N9JusPofaNJyExgZbeVdHJ98s+hlI8rfSFEihDiDSFEFyFEeyFEMLrgPwk8L4RIEkIk69tWHCdV0iwtLZk9e/YTtwcFBbFixYqnrpglq2xKUl5CCGK/XkHEtGmYN22K548/PhL452POM3TPUDKyM9j0wiYZ+Pmg18NZQohE4OeiaiuP1q9fz3fffZf7xGxGRkZuJc3MzEw++ugjli1bBkBoaCiGhoa5Uze1Wi2NGzdm9erVALLKpiT9hzYri8gPPyL5r7+wefVVqi+Yn6ekAsDBkINMOzINpypOrO2+tsIuelLU5BO5erp37x6jR49m8ODBT9znxRdf5NKlS8yYMYMdO3Zw+/ZtGjduDOimaIKssilJ/5V59SqRc+eRefky1aZMweG9R2/I/nz9Zz459QkN7RuysttKHMwdSqm35U+5Dv2oTz8lK7BoSyubNqiP80cfPXM/Q0PDJz5Fe9/9J3O3bdtGZGQk06dP56+//gIeTNG8X2UzKCgot8pmmzZt8lTZfFKpB0mqSDTJycR+9TWJ27djaGuL64qvse7RI88+QghWnV/Fuovr6OzamaXPLaWKcZUnHFF6nHId+qVJo9E8c3bA/PnzCQ4OZsyYMWRmZnL16lV69uxJdnY2nTt3Zt68ebLKplTpCSFI3rWL6CVL0SQkYDdgANUmTnhkDVu1Vs3CEwv5/ebvvFrnVea2n1tpi6YVRrn+juXniry4xMfHU79+/afus3jxYj788EOsra2JjY1l1KhR7N69m6SkJGxy/oeWVTalyizzehBRCz8mwz8As6ZNcV+39pH6OQDp6nSmHp6KX7gfo5qNYkyzMXJKpp5koujp9OnTjBgx4pn7vfHGG4wZM4YOHXSFnm7evMlrr73Gzp07qfmfmQiyyqZUWWhSU4lbuYqE77/H0NIS548XYNuvH4rBoxMK4zPiGbd/HFcTrjK3/VzeqPtGKfS44pArZ+nh1KlTJCUlUbdu3Sfuo1Kp6NevH6+++ir9+/cnKSkJAwMD6tSpw9dff03Pnj2Ji4vL3V9W2ZQqAyEESX/+ye1eL5KwZQu2r71Grb/3YNe//2MDPzQ5lLf2vMXNezdZ7rNcBn4RkFf6ehBC4Ovr+9R9tFotgwcPpn///ly+fJnBgwcza9YsAHx8fNixYwdVq1bN3V9W2ZQquqxbt4hauIj0kycxa9gQt5UrMG/W7In7X4m7wpj9Y9AKLd/83zc0d2xegr2tuJ5ZZbM0ySqbT1ZRvw9SxaNNTyduzRriN2/BwNycapMmYvfmmyiGhk98zdHwo0w5NAV7M3vWdF9DTZuaT9xXetTTqmzKK/0yQlbZlCoaIQQp/+4l+vPPyY6MxObVV3Gc+gFGDk+eU68VWjZe3siKcyuoZ1eP1d1XU9W86hP3lwquXIa+EKJS37kvy5/OJAlAFRxM1MJFpB09imm9erguXUKVli2f+ppkVTKzjs7iUOghenn2Yn6H+XIOfjEod6FvZmZGfHw8Dg4OlTL4hRDEx8fLwm1SmZV57RrBQ4aCEDh99CF2gwahPGPq8fWE60w+NJnI1EhmtpnJoPqDKuXPd0kod6Hv5uZGWFgYlWmBlf8yMzPDzc2ttLshSY9QR0QQOvJ9DCws8Ny2FeN8lA/ZeXMnC08uxMbEhk09N8kbtsWs3IW+sbHxI/PbJUkqfZqkJELeG4k2I4MaP/zwzMDP0mSx+PRifgn6hTbObfiiyxeyhk4JKHehL0lS2aPNyiJ07FjUISG4b9iAWb0nP8MCEJEawZRDU7gSf4URjUcwznucLKlQQuR3WZKkQhFaLREzZpLhH4DLl0uxaNvmqfsfCz/GDL8ZaLQavnr+K7p6dC2hnkogQ1+SpEKKWbyYlL//xnH6dGx6937iflqhZd2Fday5sAYvOy98fXzxsPYowZ5KIENfkqRCiN+0mYQt32H31lDs3x7+xP3uZd5j5tGZHAs/Rt9afZnTfg7mRuYl11Ep11Nr7yiKYqQoSoiiKIdy/jRRFGWBoihnFEVZ9dB+erdJklQ+Jf/1FzGLF2P1wgs4zZz5xCmWV+Kv8ObuNzkdeZo57ebwSadPZOCXomcVXGsKbBdC+AghfAAToBPQBohRFKW7oigt9W0rnlOSJKm4pZ06TcSMmZi3aonLF4sfWyxNCMGvQb8y9K+hCARbem6hf73+cv59KXvW8E47oI+iKM8Dl4DrwP+EEEJRlH+AXkBSIdr2/fcNFUUZCYwE8PCQ432SVNZkXg8ibNw4jGt44L5qFQampo/uk53JopOL2HlrJx1cOvB558+xM7Mrhd5K//WsK/0zQHchRBvAGDAHwnO2JQBOgEUh2h4hhFgvhGglhGhVrVq1Ap+QJEnFRx0VRejIkRiYm+Oxfv0jq1uBbsGT4X8PZ+etnYxqNorV3VbLwC9DnnWlf1EIkZXztT8Pgh/AEt0vjdRCtEmSVE5okpMJfW8k2tRUamz9AWMXl0f20QotM/1mEpgQyHKf5XSr0a0Ueio9zbOC93tFUZopimIIvILuar1TzrZmwF0goBBtkiSVA1qVirBx48m6exe3lSswe8JSoSvOreBg6EGmt54uA7+MetaV/sfANkABdgGLAD9FUb4Ceub8CQY+07NNkqQyTmi1RM78kPTTp3FZ8gUW7ds/dr8/bv3Bhksb6Fe3H4PqDyrhXkr5VeBFVBRFMQd6A2eFELcL2/Y0j1tERZKkkhX9xRISNm6k2gdTqPree4/d50LsBd75+x2aVmvK+h7rMTY0fux+Usko0kVUhBAZwK9F1SZJUtmV8N33JGzciN2gQTi8++5j94lKi2LigYk4VnHE18dXBn4ZJ5/IlSTpsZL/+Zfozz7Dqkd3nGZ99Nj59enqdMYfGE+mJpNvX/gWWzPbUuipVBAy9CVJekS6vz8R06Zh7u2Ny5Ilj13PViu0zDo6i6DEIFZ0XUFt29ql0FOpoOS0SUmS8si8epXQMWMxdnXFbdVKDJ6wStvq86vZF7KPKS2n0MWtSwn3UtKXDH1JknJlXr9OyNvvYGhpiceGb6VpPFUAACAASURBVDCye/xDVXvu7GHdxXW8WudV3mr4Vgn3UioMGfqSJAGQdeMGIcPfRjE3x2PL5ieufHUp9hJzjs2hhWMLZrebLWvplDMy9CVJIuv2HYLffgfFyIgamzdh4u7+2P2i06KZeHAiVc2r4vu8LyaGJiXcU6mw5I1cSarkVMHBhAwfDkLg8d0WTDw9H7tfRnYGEw5OIE2dxroe67A3sy/RfkpFQ4a+JFViqrAwgoe/jVCr8diyGdNatR67nxCCOcfmEBgfyNddv8bLzquEeyoVFRn6klRJqSMiCHlrGCI9HY8tmzGr++TFzNdeXMs/d/9hcsvJ+Lj7lFwnpSInQ1+SKiF1dDTBw4ajSU3FY9PGJxZQA/jn7j+sPr+al2q/xNuN3i7BXkrFQYa+JFUy6pgYQoYNR5OQgMemjZg3avTEfa/EX2H20dk0q9aMee3nyZk6FYAMfUmqRLLj4wl5+x3UMTF4bNiAedOmT9w3Nj2WCQcmYGdmx/Lnl8uZOhWEDH1JqiSyExN1gR8ejsc366nSwvuJ+2ZmZzLx4ERSVCl83+t7qppXLcGeSsVJhr4kVQKae/cIeWcEquBg3NetpUrr1k/cVyu0zD0+l0txl1j+/HLq2dcrwZ5KxU2GviRVcJrkZEJGvIvq5k3c1qzBol27J+4rhOBL/y/Zc2cPE1tMpJuHXP2qopFP5EpSBaZJTSX0vZFkBgXhuuJrLDt1fOr+Gy9v5Lur3zGo/iBGNB5RQr2USlK+Ql9RFCdFUc7lfP2toignFEWZ/dB2vdskSSoe2rQ0Qke+T8aVK7gt98XKx+ep+/924zeWn11Or5q9mNFmhpypU0Hl90p/KWCuKMprgKEQoj1QS1EUr8K0FccJSZIE2owMQkeNJuPCBVyXLsWq29OHafaH7GfBiQV0dOnIJx0/wUCRgwAV1TP/ZRVF6QqkAVGAD/BzzqZ/gU6FbHvc+41UFMVfURT/2NjYgpyLJFV6QgjSTp4iZMS7pAcE4LJ4MdY9X3jqa85EnWH64ek0dmjMMp9lcrnDCu6poa8oigkwB5iZ02QBhOd8nQA4FbLtEUKI9UKIVkKIVtWqVSvo+UhSpaRJTSNh2zZu9+1LyPDhqG7fxmXxYmz69H7q6wLjAxl/YDxuVm6s6raKKsZVSqjHUml51uydmcBqIcS9nPG9VMA8Z5slul8ahWmTJKkQsm7fJnHbdpJ+/x1tWhpmjRpR/dNPsX6x1xNXvLovJDmEUftGYWVixboe6+T6tpXEs0K/O9BVUZSxQHPAAwgFTgLNgOtAGLqhGn3aJEkqIKHRkHroEIlbt5J2/ASKsTFWvXpiP3gwZk2b5usGbGx6LCP3jkQrtKzrsQ5nC+cS6LlUFjw19IUQuQtfKopyCHgJ8FMUxQXoBbQDRCHaJEnKp+yEBO79+j8Sf9xOdkQkRs7OVJs0Cds3+mHk4JDv4ySrkhm1bxQJmQlsfGEjtWweX05ZqpgUIUTBXqAodkAP4IgQIqqwbU/TqlUr4e/vX6D+SVJFk3HpEok/bCV5zx6ESkWVdu2wGzQQq65dUYwK9nxlZnYm7+99n4txF1ndbTXtXdoXU6+l0qQoSoAQotXjthX4iVwhRCIPZuEUuk2SpEdps7JI3rOHxG3bybx4EYMqVbDt9zp2gwZhWqeOXsfM1mYz7fA0zsWcY8lzS2TgV1KyDIMklTHpZ88SPnkK2dHRmNSsidPs2di88jKGlpZ6H1MrtMw7Po9DYYeY3XY2L3g+fRqnVHHJ0JekMkIIQeLWbUR//jnGLi64f7sBiw4diuTJWN8AX3bd2sWY5mN4s/6bRdBbqbySoS9JZYA2I4PIefNI3vUHlj4+uHyxGENr6yI59sbLG9l8ZTMD6w9kVNNRRXJMqfySoS9JpUwVGkrY+AlkXb9O1QnjqTpqFIpB0TzG8vuN3/EN8KWXZy9mtpkp6+lIMvQlqTSlHjlC+NRpALivW4tlly7PeEX+HQg5wPwT8+ng0oFPOsl6OpKODH1JKgVCqyVuzRriVq7CtF493FZ8jYm7e5Ed3z/Kn2mHp9HYoTG+Pr6yno6US4a+JJUwTXIyEdNnkHroENYv9aX6ggUYmJs/+4X5kKxK5ttL37I1cKuspyM9lgx9SSpBmdeDCBs/HnVEBE6zZ2M3eFCRjLOrNCp+vPYj6y+tJzkrmd61ejOl5RRZT0d6hAx9SSohSbv/JHLOHAwtLanx3RaqtGhR6GNqhZa/7vzFynMrCU8Np3319kxuOZkGDg2KoMdSRSRDX5KKmVCriVm6lIQt32HesiWuvsswdnQs9HFPRJzAN8CXwIRA6tvXZ12PdXRw6VAEPZYqMhn6klSMsmNjCZ88hXR/f+yGDsVp+jQU48LdVL2WcA3fAF+ORxzHxcKFzzp/xos1X5Szc6R8kaEvScUk/ew5widNQpOcjMuSL7Dp27dQx4tIjWDluZXsvr0bKxMrpraayoD6AzA1NC2iHkuVgQx9SSpiQggSt28n+rPPMa5eHc9v1mNWr57ex0vKSmLDpQ1sC9wGwPDGwxnReAQ2pjZF1WWpEpGhL0lFSKtSEfXxxyT9+j8snuuC6xdfYGijXzhnabLYHrid9ZfWk6pK5aXaLzHOe5xc8EQqFBn6klREsmNjCRs/gYzz53EYPYpq48frVU5BCMFfd/7iq7NfEZkWSSfXTkxqMYl69vp/WpCk+/IV+oqi2AMtgXNCiLji7ZIklT8Zly4TNm4cmuRkXJf7Yt2zp37Hyc5g0clF7Lq1i4YODVnYcSFtq7ct4t5KldkzL0NyVrvaDbQBDiqKUk1RlG8VRTmhKMrsh/bTu02SyrOkXbsIHjIExdAQz21b9Q780ORQhv41lD9u/cHoZqPZ3nu7DHypyOXnSr8pMEUIcTLnF0BXwFAI0V5RlI2KongBTfRtE0LcKK6Tk6TiJDQaYr5cRsLGjVRp3RrXr5ZjZG+v17EOhx7mQ78PURSFVd1W0dmtcxH3VpJ0nhn6QojDAIqidEF3tW/PgyUP/wU6Ad6FaMsT+oqijARGAnh4eOhxSpJU/DRJSYR/MJW0o0exGzQIpw9n6jX/XqPVsOr8Kr659A0N7BuwzGcZblZuxdBjSdLJ75i+ArwJJAICCM/ZlAC0ACwK0ZaHEGI9sB50C6MX6GwkqQRk3bpF6JgxqCMicf54AXb9++t1nMTMRGYcmcGJyBO85vUaH7X9SM65l4pdvqYWCJ2xwEWgA3C/JKBlzjFSC9EmSeVGyoGD3O3/JtrUNGps2ax34F+KvUT/3f0JiA5gQYcFLOiwQAa+VCLycyN3hqIob+X81Rb4HN2wDEAz4C4QUIg2SSrzhBDErV1H2NixmHh6UvPXX/QqmCaE4OfrPzPs72EYKoZ89+J3vOb1WjH0WCq3hIA7fhByslgOn5/hnfXAz4qivAtcBnYARxRFcQF6Ae3QDfn46dkmSWWaNj2diFmzSNnzN9Z9+lB90UIMzMwKfJyHp2N2dO3I4s6L5VO10gNCwI1/we9LCD0FXv8Hg38p8rdRhCj4sHnOLJ4ewBEhRFRh256kVatWwt/fv8D9k6Siog4PJ3TceLKuXcNx6gfYv/OOXvXvQ5NDmXxoMkGJQYxqNopRzUbJAmmSjlYDV3eC3zKIvgQ2HtBxAngPAWP9FtdRFCVACNHqcdv0eiJXCJHIg1k4hW6TpLIo7fRpwidOQmRn475+HZad9ZtGKadjSo+lUcPFn+CoL8TfBAcveGUNNHkDinF5S1mGQZL+QwhB4rZtRH/2OSYeHritWolpzZoFPo6cjik9ljoDzn4Px7+GpFBwbgpvbIEGfcHAsNjfXoa+JD1EdfcukfMXkH7yJJbPPYfL0iUYWlkV+DhyOqb0iMxk8P8WTqyCtFhwbwd9fKFOdyiCJTPzS4a+JAFCpSJ+4ybiVq9GMTXFef58bPu/oVfBtOMRx5l7bC6JmYks6LBAzs6p7NIT4OQaOL0OMpOgdlfoPBVqdHhi2GeqNaRkZlPNqugvFGToS5Ve+rlzRM2dS9aNm1j17InTRx/qtZxhmjqNpf5L+TXoV2ra1OSrrl/RyKFRMfRYKheSI+HESvDfBOo0qN8HOn8Ark+e6hsSn84Pp4L52T8Un7rVWD7Au8i7JUNfqrQ0KSnELFvGvR9/wsjZGbfVq7Hq+rxexzoRcYJ5x+cRnR7N243eZqz3WDmcU9kIAYl3IPg43D4MV3foZuY06QedJoPj4xer12gFh4Ni+O5EMIeDYjFUFF5o5MzANsVThkaGvlTpCCFI+Xcv0YsWkR0fj/1bQ6k2YQIGFhYFPlaaOo1l/sv4OehnPK092dJzC80dmxdDr6UyRwiIC4K7R3VBH3wcUiJ028ztoflg6DgR7B8/CSAhTcVPZ0LZeiqYsMQMHK1MmdjNi4FtPHCyLvhzIPklQ1+qVNSRkUQtXETqgQOYNmiA2+rVmDdprNexTkeeZu7xuUSkRjCs4TDGeY/DzKj4flilUqbVQPSVnIA/CsEnID1neRFLZ/DsqBunr9EJqtaFx9wPEkJwPvQe358IZvelSFTZWtrXcuCjFxvQo6ETxobF/+yGDH2pUhAaDYlbtxG7fDlCq8Vx2jTsh72FYlTwH4F0dTq+Ab78eP1HaljXYEuvLXg7Fv3Yq1TKNGqIvJgT8Mch5ITuRiyArQd49YAaOUFvX+upM3AyVBr+uBDBdyfvcjk8GUtTIwa2dmdIuxp4ORV8dlhhyNCXKrzMa9eInDOXzEuXsOjcGed5czFx02++/JmoM8w5NoeI1AiGNhzKeO/xmBvp99SkVMYIATGBcOuA7k/ISd0NWACHOtDwlQchb+uer0PeiUvjh5PB/OIfSnJmNvWcrFj0SmNe8XbF0rR04leGvlRhaTMyiFu1ivhNmzG0scFl6VKse7+oVxmFdHU6X539im3XtuFu5c6mnpto6dSyGHotlai0OLh9CG7u1wV9ak5lmKr1oPnAnJDvCFZO+T5kYpqKY7fi+OlMKH434jAyUOjZ2Jm32nvS2tNOr///ipIMfalCSj16jKj581GHhWHT73Wcpk7F0NZWr2MFRAcw59gcQlNCGdxgMBO8J1DFuEoR91gqEdlZumJm96/mIy/o2s3toJaPbg597a5gk/9PghkqDWfuJnDsVhzHbsZxJSIZIcDZ2owpPeoyoI07jlZl516PDH2pQlGFhBD9xRek7tuPiacnHt9twaJNG72OlZGdwddnv2Zr4FZcLV3Z+MJGWju3LuIeS8VKCIi78SDk7x7VDdkYGIF7W+g6Wxfy1ZvnuwRCtkbLxfAkjt2I49itOM4G30Ol0WJsqODtYcfk7nXpWMeBZm62GJXAjdmCkqEvVQia1DTi160jYfNmMDam2uTJ2A8fhoGpfnPlz0afZe7xuQQnBzOg3gAmt5wsr+7Li9RYuHsEbh3U/UkO07Xb14bmg3QhX7MzmObvBqoQgpsxqRy9Gcexm/Gcuh1PSlY2AA2rWzO8oycdajvQpqY9VUzKfqSW/R5K0lMIrZaknbuIWfYlmtg4bF5+mWpTpmDsVPAnakG3otW6i+s4HHYYV0tXvv2/b2lTXb9PClIJyUiEu8fgzhG46wcxV3XtpjZQ6znoMhVqPw92nvk+ZPi9DI7fjOP4rXiO3YwjJiULAA/7KvRp5kLHOg60r+WAg2X5ewBPhr5UbmWcP0/Up5+RefEiZk2b4r5yJebNmul1rPMx51l7cS3Hwo9hY2rDeO/xDGkwRF7dl0VZKbo58neP6II+8iIgwMgcPNrpShPXfA6qNwPDZ0ecEIJbsWmcuZvA6Tu6P+H3MgBwsDChQ52qdKztQMc6VXG3L///P8jQl8oddXQMscu+JGnnLoyqVcNl8edY9+2rV3G0gOgA1l5Yy8nIk9iZ2jGpxSQG1B+AhXHBn86Viok6Q3fz9U5OyIefBaEBQxNwawM+H+qGa1xbgtGzr7yzNVoCI1M4fTeBM3cSOHM3gfg0FQBVLU1o7WnPiE41aVfLgfrOVhgYlO5sm6L2zNBXFMUG+BEwBNKAN4E1QEPgTyHEopz9vtW3TZLyQ5uVRcKmzcStXw9qNQ4jR+IwciSGlgULaCEEZ6LOsPbiWs5EncHezJ4PWn5A/3r95ZV9aRMCUqIg7rpunvwdPwg7DRoVKIa6YmWdJkHNLrrAN3n2v1emWsPFsCTO3E3g1J0EzgYnkpozJu9mZ85z9arRxtOe1jXtqVXVotSnVBa3/FzpDwaWCSH2KoqyBhgAGAoh2iuKslFRFC+gib5tQogbxXVyUsUghCBl715iFn+BOjwcqx7dcZw+HRP3/D0g8/BxTkaeZO2FtZyNOUtV86pMbz2dfnX7yQesSpo6ExJu6WbWxN3Q1bCJvwFxN0GVkrOTAtWbQtv3wbML1Gifr5uvqmwtp+8kcOJ2HGfuJHI+7B6qbC0AdZ0sebm5C21q2tOmpj3VbSrfv/szQ18Isfqhv1YDhgDLc/7+L9AJ8ObBEogFbcsT+oqijARGAnh4FE+VOan8yLweRPRnn5F+8iSmXl54bNqIRfv2BTqGEIJjEcdYe2EtF2Iv4FjFkQ/bfMhrXq/JWjnFSQhIjXko0B8K+HshwEPrc1u7QdU6ugeiHLx0X1dvDlXs8/VWCWkqDl6LYf+1aI4ExZGalY2hgUJjF2uGta9Ba097WnvaY2dhUjznWo7ke0xfUZT2gB1wFwjPaU4AWgAWhWjLQwixHlgPuoXR830mUoWSnZhI3IoVJP74E4ZWVjjNnYNd//4FqpUjhOBI2BHWXljL5fjLVLeozpx2c3ilziuYGMof/mKReBcCd8P1PRB1EbKSH2wzMteVM3BtCc0G6IqSVfXStZkUfIjuZkwq+wJj2B8YzdmQRLQCHK1M6dusOt3qO9G+tgMWpVTqoCzL13dEURR7YAXwOjAFuP+ZyBIwAFIL0SZJubITEkj47jsSt25Dm56O3aBBVBs3tkBP0wohOBh6kLUX1hKYEIirpSvz28/npdovYVyMC05XSvfr1QT+Adf+gKhLunanxtC0f85Ve84fa7fHVp7ML7VGN2yzLzCa/YExhCSkA9DIxZpxXb3o3sCRxi42Fe7Ga1HLz41cE+AX4EMhRLCiKAHohmVOAs2A60BYIdokCXVEBPEbN3Hv118RWVlYde9O1fHjMKtbt0DHuRx3mSVnlnA25iweVh4s7LiQ3rV6Y2wgw77IaLUQHgCBu+Dabki4rWt3bws9FkKDPrqqk0XgXrqKQ9dj2RcYzeHrsaRkZWNiZEDH2g6M7FKLbg0cK+W4fGHk50p/BLphmFmKoswCNgFDFUVxAXoB7dANzvnp2SZVYlm3bxP/zQaS/vgDAJu+fXF4711MaxUsNKLSovjq7Ffsvr0bezN75rafy6t1XsXIQH68LxIata6EwbXdcO1PSInUlTKo2QXaj4P6vcHKufBvoxVciUji+K14DlyLISA4EY1WUNXSlF5NnOnewIlOXlXLxZOvZZUiRMGHzRVFsQN6AEeEEFGFbXuSVq1aCX9//wL3Tyr7Mi5dJn79elL27UMxNcX2jTdweHs4xi4uBTpOujqdjZc3suXKFrRCy1uN3mJE4xFYmlgWU88rEVW6rl7NtZwx+sx7unF5r+5Qvy/U/T9dobJC0GoF16JSOH4rjpO34zl1J4GUTN10yvrOVnRv4ES3Bo40c7OVwzYFoChKgBCi1WO36RP6JUWGfsUihCD91Cni168n7fgJDKytsRs8CPuhQzGyz98sjfs0Wg27bu3i63NfE5cRRy/PXkxsORFXS9di6n0lkZUKQX/D1Z1wcx+o08HMBur2ggZ9dXVr8jE3/knu34A9fiueE7fiOXUnnsR0NQCeDlVoX9uBdrV0JQ4ci3HJwIruaaEvPyNJxU5otaQePEjcuvVkXryIYbWqOE6biu2bb2JoWfAr8lORp1jqv5RrCddoWq0pvj6+cl3awshKgaB/4MrvuqDPzgRLJ2g2UDc+79kZ9LwBLoTgbnw6x2/FceJWPCdvJxCXqqtj42prTrcGTnTICXoXWzk2XxJk6EvFRqjVJP35J/EbNqC6eQtjd3ec58/H5tVX9Kp+eSfpDssClnEo9BAuFi4s6bKEFzxfqPBPUBaLrBS4/jdc3fFQ0DtDi7d0K0R5tMt3qeH/SspQs+9qNEdv6oI+KjkTACdrUzrVcaBD7aq0r+1QIerYlEcy9KUip0lKImnXHyRs2oQ6IgLTunV1q1b1fEGvNWnvZd5j7cW1/HTtJ0yNTJnUYhJDGg7B1LD8VTgsVZnJuqGbKzlBr8nKCfph0OgVcG+n95TK+0H/56VI/G7EotYIqlqa0LaWAx1q64ZralaCEgflgQx9qUgItZpUPz+Sdu4i9cABhFqNubc3TnNmY+njo9cPu1qj5sfrP7L2wlpS1an08+rHmOZjcDB3KIYzqKAyk3U3Ya/u0C0JqMkCq+rQ6m3dFb17W72DPjkzJ+gvRuJ3Iw6VRourrTlvd6zJi02q08zNRoZ8GSRDX9KbEILMy1dI2rmT5D//RJOYiKG9PbYDB2Dz0suYNWqo1w+9RqvhQOgBlgcsJyQlhI4uHfmg1Qd42XkVw1lUMOkJulIHsYG64Ztb+3XFyqxcoNU7uit6tzZ6B31Kppp9gbqgPxKkC3oXGzOGdajBi02q09zdVgZ9GSdDXyowdWQkSX/sJmnnTlS3bqGYmGDZrSs2L7+MZceOKMb63fSLSY/htxu/8duN34hMi6S2TW3WdF9DJ9dORXwG5ZxGDQl3HtSzuV+oLC4IMhIe7GftCq3f1V3Ru7XWO+hTs7LZHxjN7ouRHA6KRZWtpbqNGUPb16B30+o0l9MpyxUZ+lK+aNPSSN67l6SdO0k/eQqEwLxlS5w/XoB1z54YWlvrdVyNVsPxiOP8EvQLR8KOoBEa2ldvz9RWU+nq0bXyPlwlBKTFPRrs8Td0gS80D/a1cNSVOWj40oOyBw51wK5moYP+z4uRHMoJemdrM4a0rUHvps54u9vJoC+nKulPlJQfQqMh/dQp3fDNv3sRGRkYu7tTdexYbF7qi0khqqDGpMfw+43f+e3Gb0SkRWBvZs+wRsPo59UPd+uClUyuELRaiLmiW/Yv+KhuZaj0uAfbDU3BoTY4NtRduVf10gW8Q20wz39doqeJTs5kX2A0e69Gc/xmPCqNFidrUwa18aBP0+q08JBBXxHI0Jfy0Ny7R/q5c6SfPkPyX3+RHR2NgZUVNn37YvPKy5h7e+s9ZqsVWo5HHOfXoF85FHoIjdDQtnpbprSaQlf3rpWrGJpWoytOFnxMF/Qhx3VrvQLYeoBXD91yf/fLDNu46z2F8kmEEARFp7L3ahR7r0ZzISwJAHd7c4a0q0HPxs60qiGDvqKRoV+JCSFQ3b1LxtlzpJ87S8a586hu3dJtNDLCslMnbD6cieXzz+s1r/6+2PRYdtzcwf9u/I/w1HDszex5q9Fb9PPqh4d1JVkzQZMNURdyruSP6a7ks3Qhi11NXe2aGp3As6Mu9ItJtkbLmbuJ7L0azd7AKEITdGvBNnO3ZdoL9ejewIm6TpbyZmwFJkO/EtFmZZF55QoZZ8+SfvYcGefOoUnUXV0a2NhQpXlzbF56iSotvDFr3BgDc/2fkNQKLScjTvJL0C8cCj1EtsimrXNbJrWYRFePrhW/nr1GDRHndUM1d4/plv67vyKUQx3dLBrPTlCjI9gUb+mI1KxsjgTFsvdqNAeuxZCUoc6tVDn6uTp0a+CIkyx5UGnI0K/AsuPjST+ru4LPOHuWzCtXEGpdnRMTT08sfXwwb+FNlRYtMKlZU6+FxR+WkZ3Bmagz+IX5cSTsCBFpEdiZ2jG04VBe83oNTxvPIjirMiwtHm78C0F74OaBByFftZ6utrxnR13IF0E1yqcRQhCWmMHhnKA/cUs3Pm9bxZhuDRz5v4ZOdPaqJhcYqaTkv3oFoUlNI/PqFTIvXyHz8mUyLl9GHRICgGJsjFmTJtgPewtzb2/Mvb0LXODsSUKSQ/AL98Mv3A//KH+yNFmYG5nTxrkNk1pOoptHt4p7VS8ExF7Xhfz1v3ULeAut7uGnJq9Dred1IW9ZrVjeXqMVhCakczMmlRsxqdyISeFWTCo3Y1JJU+lm99RwqMJb7WvQo6ETLWvYYWQo1y2q7GTol0Pa9HQyr13LDffMy1dQ3bmjCyHAyKU65o0aY/fmm5h7e2PWuBEGJkUTvFmaLPyj/DkafhS/cD+Ck4MB8LT25I26b9DZtTMtnVtW3BIJGrVuTP7637qwT7yra3duCl2mQb1e4NysUCtE/ZcqW0twfBo3cgL9RkwqN6JTuB2XlrvgN+hq23g5WvFGK3dqO1rSrqY9dRzl+LyUlwz9Mk6blUXWtWu54Z55+TJZt27ppvgBRo6OmDVujHWf3pg3boxZo0YYORRtmYLw1HD8wvw4Gn6U01GnycjOwNTQlNbOrRlUfxCdXTtX7GmW6QlwY2/OsM1+3bqvhqZQ6znoMAHq9iyScXkhBJFJmVwKT+JKeBJB0bqr9+D4dLK1D0qgu9mZ4+VoSZe61ahTzZI6TpbUcbTE2qwSzX6S9CZDvwwRQqAOCdHdZD17lozLl8m6cQOydYtKGNrbY9a4EVY9umPWuAlmjRph7ORY5P1IzEwkMD6QYxHHOBp+lNtJuuXw3CzdeKXOK3Ry7URr59aYG1XQUrhaLcRd143PX/8bQk/qhm0sHKHhy7qr+Vo+BV7M+2FCCMLvZXA5PIlL4UlcDk/mcngS8WkqAAwU8KxqQZ1qlvRs7IyXoxV1HC2pVc1CrholFUp+F0Z3An4VQnRWFMUY+A2wB74VQmwsTFsxnFO5IdRqMq9dJ+NsAOkBZ0k/exZNnO6BHAMrK8ybNMHynXcwa9wI88aNMapevUg/qqs1am4n3SYoMYgbiTcISgwiKDGI2IxYAIwNjGnl1Ip+dfvRybUTntaebAaQIAAAEH1JREFUFW+oQAhICoXwsxBxVvffyAu6q3kApybQ+QPdIiIu3noN29y/sXo/4P+/vTOPjeu47/hn9r4PLpdcnpIskpJqMQwo+lAToZLTujn+KGC7deoUKJoiTowmbYHmjwYp0PQfAykK/1OgQVykrYMkLZoCbfqH28ZB5VoOaqOUj1quZeiwJfHeJZfkHtx7+se8PUguZZJLccnd+QCDmfm9N/vmDfm+Oztv5jfvTK/w7swqS4bAm02C4S4Pj5zsYrTfz+k+P6ciPpy2vZ2Xr9HA9jZGDwIvAOVuzdeAy1LKbwkhXhRC/Bj40m5tUsrEvbm1g0cxmWLt7bdYMwR+7e23kWtqnrS1rw/3L57FNT6Oc3wc+9BQw7Npykgpia3FKqJeDjdXblIoqV8RVpOV44HjnO09y0hwhJHgCGPhMVzWFvN5norVCPxllS6vfDVZIXIaRn8d+sbV/q+7mDO/nM7x2s1F3rqzwpXpFa7MrLBs7A5lMQlGur388qkuRvsMge/x4bBqgdfsD9vp6ReBJ4GfGPnzwB8b6VeAiQZtF2svJoR4GngaYLCBZf4Hgfz8vJoTf/kN0m9cJnv1fTV0YDJhP3mCwOOP4zqjRN7a3d3w9aSULGYWub16m1urt9b14OPZeOW8blc3I8ERzvWdqwj8Ef8RrKYWGxPOrMLsWzUi/yas3DYOCgifgOFHlcD3jUP3abDs/AV0rlDizdtxLl2Lcel6jHemlilJsJqVwH/6/gin+/yM9vk5EfFqgdc0lY8UfSnlKlD7s94NTBvpJaC7QdvG6z0PPA9qj9yd3Mx+IqWkuLxMYW6O/Owc+blZCrNz5OfmKMzNkZuaojA7C4BwOnGOjdH5la/gPDOOc2xsV9sElq8bXYtya/UWdxJ3uL16m9uJ25V4rbBWOddhdjAUGOLC4IWKuA8Hhgk49sZXy4FBSkjMwfwV5dqgHMeuAca/UOAI9J+BB7+kBL5nDOzeXV5OciOaVCJ/TW3onc4VMZsEY/1+vvbIMOeGOxnt92O3aIHXHCx280YoCTiBFcBj5BuxHUiKiQT52dn6oj47S35+HpnJrC9ksWDt7sYSieA6cwbn6Gmc42dwnDyxI3fDJVliIb1QFfQaUZ9KTK0Tdouw0O/tZ8A7wERkggHvAEd8Rxj0DtLn6cO8x/5amk4hp1wI14r7/BVIL1bP8Q+qYZrTTyiB7x0Hd2MzmhaTWV69HuPVazFevR5jdkX97Y+GXDw23se54TBnj4f0DBrNgWc3on8Z+CTwT8AY8FqDtn1HFosUFhbIz86Sn5klPzNDfnaGQiU9Sym54fvIZMISDmONRLCfOoXnwgWsPREskR4jjmDp7Nz2OHw6n2Y6Oc2dxB2mElNMJacq6enkNPlSvnKu1WRlwDvAoHeQh3seZtA7qIJvkIg70rruh1OLMP8OzF0xBP4KRK9CuW0sDug6BSc+C5FRNTzTff+eeJ3M5ItMfhjn0vUor16L8e6MerHrd1r5xFCI3x8O88mhTr3Pq+bQsRu1eAF4UQhxDvgF4HXUkM1ubXtOKZcjf/v21qI+Pw/F4royZr8fS28v1oEBXA89hLUngrWnpyrq4fCO9nctyRKxtRhTCUPMk1PVdGKKxcziuvM9Vg8D3gGGg8NcGLxAv6efQZ8S925Xd+v12DeSSylfNVP/A9OTahx+dbp63BNRvfehT1UFPjQE5sa+8KSURBNZ3ptLcHV2latzCd6bXeVGNEm+KLGaBeODQb7+6AjnhsOc7vNj1l4nNYcYIeXOh82FEL2oHvt/SClXGrVtxcTEhJycnNxx/dJvvsmt33yqajCbsXZ3Y+3txdLbg7WnF2tPD9a+3oqwmz13n3OdLWaJZ+IsZ5e3jrNxljPVOFfKVcqbhImIK0K/t78yHNPvqaZ9Nl/rTYfcilJJDdFMTyqRn7oMC/9X3RgkeAz6J9S4e/dpJfLuzoYvm8kXuTaf5L25Va7OJrg6p0S+PHUSoMfv4GTEy4mIjwePBXnoWEj7qNEcOoQQl6WUE3WP7Ub094vdin4xkSD5X69g7e1RQh8OI8zVnnJJlkjkEixllohn4ipkVbyUWdok3vFsfN04+kb8dj8Be4CAPUDQHiTgUHGvp1eJu7efXndve/mLryUZNQR+stqLL8+Dt/vVC9a+CSX0fWcaFvhiSTKzvMb7c0rYy734D2IpygtbnVYzIxEvpyJeTka8nOzxcTLiJeBqUT9BmrbibqLfkl2YuCXLS0OrxDO3WPpwifhV1RMvi/xydpmiLNYt67K4CDqCBO1BQo4QQ4EhJeaO4CZRDzgC+Gy+1h1T3ynFgpoSWd6vdeYNJfTLyj8PwqzG3EefUHu29k2oIZpdLniKJrJ8EEttCreW0ut80gx2uDgZ8fK5j/Uqke/xMdjh0sM0mrakJdVqPj3Ps68/C4DP5qPD0UHQEWTQO8hYeKySD9gDlXSHo4OAPYDDov2K3xUpIbkAi9eNcA0Wb6j00gfVl6ygNubun1Cbc/dPQM/HwbazF5/L6VxFzD+MpbhZky57kgSwmU0cCbk42unmkZNdHO10M9Lt5UTEi0cPz2g0FVryaRgJjHDxNy7it/tbb8HRfiAlZFYg/kFV0BevGxt036j6iQfleKzjPugcUbs/hYaqYZvDNKWSclNwPZrguuFJ8vpCkg9iKeLp6peIScBAh4ujITcPHO3gWKe7EnoDTt1z12i2QUuKvtVspdPZ+Iu/lkFKyCaUu4HUIqSiRtoIlXRUzXdPxaCYrfkAAYEBJeQDDxqiflzt3+rv3/berblCiQ8XU8o98HyS61El7jejSbI1wzGdHjtDXW4+M9rDsZAh7GE3A0EXNov2B6/RNEJLin5Lk1+DtWW1iXbGiNfi9W2p2BYiXoPVrRYuuTrV5h+RUXCFwB2G4FEl8B3HwLo9j5rFkmQxmWV6eY2b0VRF2G8sJLm1lKa4wUXwUJeHTxwPMdTlYbjbw/GwR79M1WjuIVr0m4mUSpyTC5CcVz3tSjoGa0ubxbyQ2frzhFktTHIGwREAX6/a3KMs6u6wGnJxhYy4c0dj7LlCifnVDPOrGWZXMsytqFjl15hbyTCfyK4TdotJVMbXPzvaUxH242GP9iKp0TQBLfp7iZSqJ55ZUSG1oEQ8FVVCnowatpq04eVyHSYreLrA2aFEvHNICXlZzJ3BqrjX2uxe2MVc/2JJspjKEk2osGDEcysZ5lar4h5Lbv614LSa6Qk4iPgcPHw8RI9fpSN+J8c63RwJubDqLfo0mgODFv1aCjk19p1dUR4as6sqzqwY6ZWa/MqGvBHXE3EAk0VtwuEJg6db+Wn3dKngNmzlvCOwK/HeSCpbWCfi0USmmk5mWVhV8WIyS6nOcg2/06pE3O/g/l4fEb/DyDsNYXfgc1jaZ1GZRtMCtKbory2rlZ5l0c4mVDqbqIp5vWN3GzopY/OA3QcOPzh8SqRDQ0besDn86pxaMXcEGt43tVAssZTOsZg0QipLLJljMZldn0+pfDq3eS2CxSTo9Njp8tnp8Tv4WL+fsNdOl9dO2AhdXgedHrseftFoWpDWFP3FG/DDJzYYhSHWPjUMUhbl0PFqft1xb1XI7TVC3qCvlzJSSpLZAsvpPEupHPF0juV0nng6RzyVI57OV0R8KaWEvXb6Yi0WkyDksRFy2wl5bBwNuQh5DBE3BL6cDrpsmPTURo2mbWlN0Q+fgN/9mSHchojbPHsyZFJGSslavkgqWySVLZDKFUjnVDqdK5LMFlhJ51lK51hO54inatLpPMvpHPlifRcYQkDAaSXksRNy2zjR7SV0PESH20bIY6fTiEMeG51uOz6nHmLRaDTbozVF3+6BgQcq2WJJks6uF+VKnCuQzhY3iXbt8VS2QCpbJJ0rkMoV1Wfli2zHbZHFJAi4bARdVoJuG8c63Zxx26o2l00FdzXvc1r1QiONRnNPaEnRf3dmhWd+8EalB57Jlz66kIHZJHDbzLhsFtx2M267BbfNQm/AWrG5bBZ1jt2y6dxK2mbB77LiteteuEajOTi0pOj7HFYmjgRxGeLrsllw2cw1+bJA18Q2Cy67GZvZpEVao9G0LC0p+gMdLp578uPNroZGo9EcOPSqGY1Go2kjmiL6QojvCSH+WwjxJ824vkaj0bQr+y76QojHALOU8ixwnxBieL/roNFoNO1KM3r654F/NNI/Re2XW0EI8bQQYlIIMRmNRve7bhqNRtPSNEP03cC0kV4CumsPSimfl1JOSCknwuHwvldOo9FoWplmiH4SKDtn9zSpDhqNRtOWNENwL1Md0hkDPmxCHTQajaYtacY8/X8BLgkheoHPAA83oQ4ajUbTlgi5HQcye31RIYLArwCvSCnn7nJeFLjVwKU6gVgD5e81un6NoevXGLp+jXGQ63dESln3pWhTRH+/EEJMSiknml2PrdD1awxdv8bQ9WuMg16/rdAvUTUajaaN0KKv0Wg0bUSri/7zza7AR6Dr1xi6fo2h69cYB71+dWnpMX2NRqPRrKfVe/oajUajqUGLvkaj0bQRh070hRDdQohLRnpcCPEzIcTPhRB/dBfbnwkhXjbCVSHEN5p5D81ACOEXQvybEOKnQoh/FkLY6rm43mirV655d9FcGmjDZ2r+/94SQny3eXfRPHbQfpVn3MgPGm33n0KI54Xe2q4hDpXoG4u6XkA5bQP4S+B3UG4dHhdCHKtnk1L+qZTyvJTyPHAF+P6+V775fAF4Tkr5KDAHfJ4NLq63cHu9sdynm1T/g8Cu2lBK+Z2a/79LwF836waazHbab+MzDvBl4Bkp5SPAADC6z/VuKQ6V6ANF4Elg1ch3SCnvSPU2ehHwbWEDQAjxADAlpZwWQrwghHjYsP9dOd2qSCn/Skr5kpENA7/FZhfX5zfa6pRbMH45fR5ACPGtcrrV2W0blssLIfqAbinlZDu24Tbbb+MzjpTym1LK94xsCIi12/O7lxwq0ZdSrkopV2pMPxdCfFUI8RRwFPjfLWxl/gD1SwBUb/8LxnDFKSnla/f8Bg4AQoizQBC4w2YX11u6vS6XM9rp+8BTxqFfBX5y72t+cNhtGwK/B3zHSLdtG96t/eo847XlngTelVLO0KbP715wqES/Dl8GrgJfBb5t9O7r2RBCBIAuKeUNo+xF4CzwOeBf97vizUAI0YH60vsi9V1c13V7vaEcRht6hRDngStSyrV9uoWm00AbmoALwMvQvm24jfbbqtx9wNeBPzRMbff87hWHWvSllEXgfSP7w61sBr8GvFhTtgS8BPwF8IN7XtkmY/SIfgx8Q0p5i/ourjfZ6pQr8w/A39BG70d224ZG+hzwerkTYtBWbbjN9qtXLgj8PfDF8q+Adnt+9xQp5aELwMs16ReAcxuO17P9CBjfYBsHLjb7fvapzZ4B4qie5svAbwNvA88B7wF+1PuPjbaN5Z40Pi8E3MBY4NcOYbdtaJR9Fnhsw+e1VRtup/1qzn25Jv1tYLam3C8Z9rZ5fvcytO2KXCHEp4A/B74ppfz3ZtenGYg6Lq7r2eqUux/4W+C7Usrv7Vd9DyK6DRtjO221Rbm2f353S9uKvkaj0bQjh3pMX6PRaDQ7Q4u+RqPRtBFa9DUajaaN0KKv0Wg0bYQWfY1Go2kj/h/hVqF4TwmxjgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2. 数据2 中北京、上海、江苏、广东四个省的数据，画出线图、柱状图、饼图其中线图画所有年份趋势， 柱状图画2016年四省对比，饼图画所有年汇总\n",
    "import matplotlib.pyplot as plt\n",
    "gdf = pd.read_csv('e:/数据可视化/实验/gdp.csv').set_index('province')\n",
    "four = gdf.iloc[[0,8,9,18]]\n",
    "four.T.sort_index().plot.line()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f3f0162648>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "four['2016y'].plot.bar(rot = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f3f01eee88>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "four.T.sum().plot.pie()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f3f04d80c8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3. 数据1中，去掉第一行全国数据。通过散点图找出和forcible_rape最相关和最不相\t\t关的两列（相关就是y轴随X轴变化），以两个子图形式画出，且每个点大小用\t\tpopulation除以300000来控制。\n",
    "crime = pd.read_csv(\"e:/数据可视化/实验/crime.csv\").loc[1:]\n",
    "fig, axes = plt.subplots(1,2)\n",
    "\n",
    "crime.plot.scatter(x = 'forcible_rape', y = 'murder', s = crime['population']//300000,ax = axes[0])\n",
    "crime.plot.scatter(x = 'forcible_rape', y = 'burglary', s = crime['population']//300000 ,ax = axes[1])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
